A Primer on Image Informatics of High Content Screening

Zhou, Xiaobo; Wong, Stephen T.C.

doi:10.1002/9780470229866.ch3

Cited by 5 publications

(3 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, 3D images over time are difficult to interpret manually and the result suffers from subjectivity. Therefore, computer-based image analysis is required to cope with the enormous amount of image data and to extract reproducible as well as quantitative information (Peng, 2008;Zhou & Wong, 2008;Hamilton, 2009;Swedlow et al, 2009;Rohr et al, 2010). Automatic analysis of multidimensional live cell microscopy images requires different computational methods.…”

Section: Computational Methods For Quantitative Image Analysismentioning

confidence: 99%

“…Filters that are not based on convolution are called nonlinear filters. A nonlinear filter that is often used to remove the pepper-noise generated by CCD detectors in optical fluorescent microscopy is the median filter (Zhou & Wong, 2008). This median filter can preserve high frequency information describing cell edges in high content microscopy images.…”

Section: Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

On-Chip Living-Cell Microarrays for Network Biology

Willaert¹,

Sahli²

2011

Bioinformatics - Trends and Methodologies

View full text Add to dashboard Cite

Bioinformatics-Trends and Methodologies 610 analysis of multiple cell types or stimuli. Experiments with multiwell plates are typically integrated in a robotic analysis platform. Two major drawbacks of robotic platforms are the expense of the instrumentation, and the cost of experimental consumables. The use of microarrays was first reported in 1989 (Ekins et al., 1989). The variety and diversity of microarrays has become impressive. Three main types of microarrays have been developed: DNA microarrays, protein microarrays, and cell microarrays (Barbulovic-Nad et al., 2006). Several different approaches to cell microarrays have been explored to investigate gene expression, cell-surface interactions, extracellular matrix composition, cell migration and proliferation, the effects of drugs on cellular activity and many other areas (Angres, 2005). There are two fundamental methods to produce cell microarrays: the indirect and the direct method. The indirect method-i.e. the "reverse transfection" method-was developed by Ziauddin and Sabbatini (2001). In the direct method, the cells are printed onto a substrate. In few cases contact-based microarrayers are used, but more often non-contactbased devices are used. Miniaturisation of cellular assays via cell microarrays increases assay throughput while reducing reagent consumption and the number of cells required, making these systems attractive for a wide range of assays in drug discovery, toxicology, and stem cell research (Fernandes et al., 2009). Cell microarrays have been developed for highly parallel, highthroughput analyses of cell phenotypes (

show abstract

Section: Computational Methods For Quantitative Image Analysismentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

On-Chip Living-Cell Microarrays for Network Biology

Willaert¹,

Sahli²

2011

Bioinformatics - Trends and Methodologies

View full text Add to dashboard Cite

show abstract

“…For example, there are some discussions [6] on the integration of array data (e.g., SNP array, gene expression array, microRNA array, axon array, and tile array). Although it is beyond the scope of this special issue, we also recommend reference [7] , which presents the advantages and disadvantages of different platforms used for image analyses—another area of interest in the field. Ultimately, we expect that advancements in cancer bioinformatics will contribute significantly to our understanding of the complex mechanisms of cancer and lead to better therapeutic strategies.…”

mentioning

confidence: 99%

Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules

Zhou¹

2013

Chin J Cancer

Self Cite

View full text Add to dashboard Cite

In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.

show abstract

Computational Imaging and Modeling for Systems Biology

Zhou

Wong

2010

Elements of Computational Systems Biology

View full text Add to dashboard Cite

A Primer on Image Informatics of High Content Screening

Cited by 5 publications

References 60 publications

On-Chip Living-Cell Microarrays for Network Biology

On-Chip Living-Cell Microarrays for Network Biology

Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules

Computational Imaging and Modeling for Systems Biology

Contact Info

Product

Resources

About